1. Field of the Invention
The present invention generally relates to mobile communication systems, and particularly to an apparatus and method for calibrating a reception signal in a Smart Antenna System.
2. Background of the Related Art
Performance and capacity in a mobile communication system is fundamentally controlled by radio wave channel characteristics, such as same channel interference generated between cells or within a cell, path attenuation, multipath fading, delay of a signal, Doppler spectrum, and shadow phenomenon.
Currently, mobile communication systems apply a plurality of techniques including power control, channel coding, RAKE reception, a Diversity antenna, cell sectorization, frequency division, spread spectrum and the like as compensation techniques in an attempt to overcome limitations on performance and capacity. However, as mobile communication services gradually become diversified, demand for these services greatly increases. It is therefore considered that it will gradually be more difficult to satisfy increasing needs for high performance and high capacity of a mobile communication system with only existing techniques.
In addition, compared to existing cellular phones and personal portable communications, 21st century mobile communication systems will provide multimedia communication services requiring high quality and much greater capacity, and even for tone quality these systems will be required to offer high-quality voice service which is at least as good as the tone quality in wire communications. Also, in a mixed cell environment in which various service signals are mixed, 21st mobile communication systems will have to attenuate the effects of strong interfering signals due to high-speed data which have transmission power and transmission bandwidth which are relatively great. Mobile communication systems will also have to provide satisfactory service in so-called hot spot or shadow areas.
In an attempt to overcome performance degradation due to interfering signals and other channel characteristics, a smart antenna has been used and evaluated as a prospective core technique for widespread use in commercial systems.
Unlike the case where multipath signals are coupled by two existing diversity antennas, in a smart antenna system an array antenna having a plurality of antenna sensors is used, where the sensors are arranged at certain intervals and an advanced high performance digital signal processing in baseband is used. The smart antenna increases a degree of freedom in design by adding space processing capability to the mobile communication system, thereby improving the entire performance of the system. That is, instead of emitting an omnidirectional beam, a smart antenna emits a directional beam to only an affected subscriber so that interference between signals is minimized for all subscribers who are operating in a sector. This results in an increase in communication quality and system's channel capacity.
When communicating using a related-art smart antenna system, a receiver calculates a weight vector needed in a signal process or extracts a specific parameter of a channel such as direction of arrival (DOA) or the like, based on a received signal. If characteristics of each antenna are different, accuracy in a signal process is degraded. A calibration method for maintaining characteristics of each receiver should therefore be performed.
In general, there are two related-art calibration methods. One uses a reference path and the other uses a locally generated reference signal. In the method which uses the reference path, a received signal passes through a reference path and an array to be calibrated at the same time. Then, using the signal which passed through the reference path as a reference, values for calibrating the array are obtained using an algorithm such as LMS (Least Mean Square) or NLMS (Normalized LMS).
The LMS is a representative algorithm of adaptive filtering and is similar to an adaptive filter coefficient algorithm which has been developed by Widrow. But, unlike the Widrow filter, in the LMS, Mean Squared errors (MSE) have no two-dimensional factor, and therefore the LMS shows excellent performance simplifying an algorithm structure and in a calculating speed for data processing.
The NLMS is an algorithm performed based on a plurality of adaptive filters, and more specifically which controls a convergence constant that affects convergence speed and stability of the adaptive filters, and then plans an optimum filter in a system by controlling a convergence constant which affects fast convergence of adaptive filter coefficients in order to overcome system degradation due to a fixed convergence constant. Also, the NLMS is representative algorithm of a method where a convergence constant is changed into a proper value in every sample by power of an inputted signal which is repeatedly calculated.
Through the methods of using the algorithms and the reference path, the output of the array and the output of reference RF (Radio Frequency) are maintained the same. However, this method has a defect in that the reference signal is unstable.
FIGS. 1a and 1b illustrate a calibration process in which a locally generated signal is used as a reference signal. With reference to FIG. 1a, in order to generate a reference signal, an RF transmission block 10 is positioned outside of an array. An output generated from the RF block is input into a splitter 20. The output passes through the splitter and is divided into several signals (1:N). The divided signals are then input into respective portions of an array 30.
If an ideal splitter is used, the size and phase of each divided signal has to be the same. Accordingly, if an error of the splitter is ignored, since the same signals are inputted to the array, it is very easy to discover and calibrate an error generated at the array.
An actual splitter is different from the ideal one, so the signals output from the splitter have sizes and phases which are different from each other. That is, when a signal output from array 30 is measured after a signal has been input to an array 30 via RF block 10 and splitter 20, inherent errors of the splitter and the array are mixed in the output signal, and so the error of array 30 cannot be calibrated.
In order to properly calibrate the error of the array, the error of the splitter first has to be calibrated. Therefore, as shown in FIG. 1b, a complicated process of measuring an output of the array has to be performed twice; that is, an output of the array is measured and then the output of the array is measured again with a cable changed.
Through the twice-performed process of measuring the output, an inherent error of the splitter is detected and calibrated and then an error of the receiver is calibrated, thereby maintaining the same characteristics of the array.
In the smart antenna system calibrating process described above, the method of using a reference path is defective because the reference signal itself is unstable. Consequently, it is difficult to perform accurate calibration. Also, the method of using a locally generated reference path is defective, because a complicated measuring process has to be performed twice in order to calibrate an error generated when a signal passes through a splitter.